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About the Role
We are seeking a motivated and analytical Data Scientist to transform complex datasets into actionable business insights. The ideal candidate will have hands-on experience with Python, SQL, statistical analysis, machine learning, and data visualization tools such as Tableau. In this role, you will work with cross-functional teams to analyze business performance, develop predictive models, identify trends, and support data-driven decision-making.
Responsibilities
- Collect, clean, process, and analyze structured and unstructured datasets from multiple sources.
- Develop statistical models and machine learning solutions to solve business problems.
- Use Python libraries such as pandas, NumPy, scikit-learn, and SciPy for data analysis and modeling.
- Write optimized SQL queries to extract, transform, and analyze data from relational databases.
- Perform exploratory data analysis (EDA) to identify patterns, trends, correlations, and anomalies.
- Apply statistical methods, hypothesis testing, and experimental analysis to support business decisions.
- Build, validate, and evaluate predictive models using appropriate performance metrics.
- Create interactive dashboards, reports, and visualizations using Tableau.
- Communicate analytical findings and recommendations to technical and non-technical stakeholders.
- Collaborate with data engineers, business analysts, product managers, and engineering teams.
- Monitor model performance and maintain analytical workflows as business requirements evolve.
- Document methodologies, assumptions, results, and recommendations.
Requirements
- 1–5 years of professional experience in data science, analytics, machine learning, or a related field.
- Strong programming skills in Python.
- Proficiency in SQL and relational database querying.
- Solid understanding of statistics, probability, hypothesis testing, and experimental design.
- Experience with machine learning algorithms, model training, and model evaluation.
- Familiarity with pandas, NumPy, scikit-learn, and other Python data science libraries.
- Experience creating dashboards and visual reports using Tableau.
- Knowledge of data cleaning, feature engineering, and exploratory data analysis.
- Ability to interpret complex data and translate findings into actionable business insights.
- Strong problem-solving, critical-thinking, and communication skills.
- Ability to manage multiple analytical tasks and collaborate effectively with cross-functional teams.
- Experience with Power BI or other business intelligence and visualization platforms.
- Familiarity with cloud data platforms such as AWS, Microsoft Azure, or Google Cloud.
- Knowledge of data warehouses and platforms such as Snowflake, BigQuery, or Redshift.
- Experience with A/B testing, forecasting, customer segmentation, or predictive analytics.
- Familiarity with Git, Jupyter Notebook, and collaborative development workflows.
- Understanding of ETL processes, data pipelines, and data quality management.
- Experience applying machine learning to business use cases such as customer churn, fraud detection, recommendation systems, or demand forecasting.
Skills
- Python
- SQL
- Statistical analysis
- Machine learning
- Data visualization
- Tableau
- pandas
- NumPy
- scikit-learn
- SciPy
- Power BI
- AWS
- Microsoft Azure
- Google Cloud
- Snowflake
- BigQuery
- Redshift
- A/B testing
- Forecasting
- Customer segmentation
- Predictive analytics
- Git
- Jupyter Notebook
- ETL processes
- Data pipelines
- Data quality management
Location
- New York City, NY, USA
- Boston, MA, USA
- Seattle, WA, USA
- Chicago, IL, USA
Work Type
- Full-Time
Experience Level
- 1–5 Years
Salary/Compensations
- $85,000–$160,000 per year
Benefits
- Competitive compensation and benefits, subject to employer policy.
- Opportunities to work on challenging data science and analytics projects.
- Professional development and career growth opportunities.
- Collaborative, data-driven work environment.
Equal Opportunity
- The employer welcomes qualified applicants and is committed to providing equal employment opportunities in accordance with applicable laws.